The Patient Experience of Acute Lymphoblastic Leukemia and Its Treatment: Social Media Review
Bibliographic record
Abstract
BACKGROUND: Adult patients with acute lymphoblastic leukemia (ALL) report substantial disease- and treatment-related impacts on their health-related quality of life (HRQOL). Patient-reported information (PRI) shared on social media may provide a distinct opportunity to understand the patient experience outside of formal research contexts and help inform the development of novel therapies. OBJECTIVE: This qualitative social media review aimed to assess PRI shared on social media websites to gain a better understanding of the symptom, HRQOL, and treatment impacts on individuals with ALL. METHODS: We identified English-language posts on 3 patient advocacy websites (Patient Power, The Patient Story, and Leukaemia Care) and YouTube that included PRI about experiences with ALL or ALL treatments shared by adults (aged ≥18 years) with a self-reported ALL diagnosis. Patients' demographic and disease characteristics were extracted from posts (where available), and the posts were analyzed thematically. A network analysis was conducted to delineate possible associations among ALL symptoms, HRQOL impacts, and treatment-related symptoms and impacts. RESULTS: Of the 935 social media posts identified, 63 (7%) met the review criteria, including 40 (63%) videos, 5 (8%) comments posted in response to videos, and 18 (29%) blog posts. The 63 posts were contributed by 41 patients comprised of 21 (51%) males, 18 females (44%), and 2 (5%) whose gender was not reported. Among the patients, 13 (32%) contributed >1 source of data. Fatigue (n=20, 49%), shortness of breath (n=13, 32%), and bruising (n=12, 29%) were the symptoms prior to treatment most frequently discussed by patients. Patients also reported impacts on personal relationships (n=26, 63%), psychological and emotional well-being (n=25, 61%), and work (n=16, 39%). Although inpatient treatment reportedly restricted patients' independence and social functioning, it also provided a few patients with a sense of safety. Patients frequently relied on their doctors to drive their treatment decisions but were also influenced by family members. The network analysis indicated that disease-related symptoms were primarily associated with patients' physical functioning, activities of daily living, and ability to work, while treatment-related symptoms were primarily associated with emotional well-being. CONCLUSIONS: This social media review explored PRI through a thematic analysis of patient-contributed content on patient advocacy websites and YouTube to identify and contextualize emergent themes in patient experiences with ALL and its treatments. To our knowledge, this is the first study to leverage this novel tool to generate new insights into patients' experiences with ALL. Patients' social media posts suggest that inpatient care for ALL is associated with restricted independence and social functioning. However, inpatient care also provided a sense of safety for some patients. Studies such as this one that capture patients' experiences in their own words are valuable tools to further our knowledge of patient outcomes with ALL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".